stock_price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sales prediction using regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all the required libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.model_selection import train_test_split\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-06</th>\n",
       "      <td>214.050003</td>\n",
       "      <td>214.419998</td>\n",
       "      <td>212.509995</td>\n",
       "      <td>213.259995</td>\n",
       "      <td>213.259995</td>\n",
       "      <td>19362300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-09</th>\n",
       "      <td>214.839996</td>\n",
       "      <td>216.440002</td>\n",
       "      <td>211.070007</td>\n",
       "      <td>214.169998</td>\n",
       "      <td>214.169998</td>\n",
       "      <td>27309400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-10</th>\n",
       "      <td>213.860001</td>\n",
       "      <td>216.779999</td>\n",
       "      <td>211.710007</td>\n",
       "      <td>216.699997</td>\n",
       "      <td>216.699997</td>\n",
       "      <td>31777900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-11</th>\n",
       "      <td>218.070007</td>\n",
       "      <td>223.710007</td>\n",
       "      <td>217.729996</td>\n",
       "      <td>223.589996</td>\n",
       "      <td>223.589996</td>\n",
       "      <td>44289600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-12</th>\n",
       "      <td>224.800003</td>\n",
       "      <td>226.419998</td>\n",
       "      <td>222.860001</td>\n",
       "      <td>223.089996</td>\n",
       "      <td>223.089996</td>\n",
       "      <td>32226700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2019-09-06  214.050003  214.419998  212.509995  213.259995  213.259995   \n",
       "2019-09-09  214.839996  216.440002  211.070007  214.169998  214.169998   \n",
       "2019-09-10  213.860001  216.779999  211.710007  216.699997  216.699997   \n",
       "2019-09-11  218.070007  223.710007  217.729996  223.589996  223.589996   \n",
       "2019-09-12  224.800003  226.419998  222.860001  223.089996  223.089996   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2019-09-06  19362300  \n",
       "2019-09-09  27309400  \n",
       "2019-09-10  31777900  \n",
       "2019-09-11  44289600  \n",
       "2019-09-12  32226700  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the CSV file\n",
    "\n",
    "df = pd.read_csv('AAPL.csv')\n",
    "df.set_index('Date', inplace=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f53832eb90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the stock prices\n",
    "\n",
    "df['Adj Close'].plot(label='AAPL', figsize=(15, 9), title='Adjusted Closing Price', color='red', linewidth=1.0, grid=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Rolling Mean / Moving Average to remove the noise in the graph and smoothen it\n",
    "\n",
    "close_col = df['Adj Close']\n",
    "mvag = close_col.rolling(window=100).mean()     # Taking an average over the window size of 100.\n",
    "# Increasing the window size can make it more smoother, but less informative and vice-versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f538771ad0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Visualizing Rolling Mean and Adjusted Closing Price together\n",
    "\n",
    "df['Adj Close'].plot(label='AAPL', figsize=(15,10), title='Adjusted Closing Price vs Moving Average', color='red', linewidth=1.0, grid=True)\n",
    "mvag.plot(label='MVAG', color='blue')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f53875eb10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Return Deviation measures the Mean of the Probability Distribution of Investment Returns if it has a positive/negative Average Net Outcome\n",
    "\n",
    "rd = close_col / close_col.shift(1) - 1\n",
    "rd.plot(label='Return', figsize=(15, 10), title='Return Deviation', color='red', linewidth=1.0, grid=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Number of days for which to predict the stock prices\n",
    "\n",
    "predict_days = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shifting by the Number of Predict days for Prediction array\n",
    "\n",
    "df['Prediction'] = df['Adj Close'].shift(-predict_days)\n",
    "# print(df['Prediction'])\n",
    "# print(df['Adj Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2487, 6)\n"
     ]
    }
   ],
   "source": [
    "# Dropping the Prediction Row\n",
    "\n",
    "X = np.array(df.drop(['Prediction'], axis = 1))\n",
    "X = X[:-predict_days]      # Size upto predict days\n",
    "# print(X)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2487,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Creating the Prediction Row\n",
    "\n",
    "y = np.array(df['Prediction'])\n",
    "y = y[:-predict_days]      # Size upto predict_days\n",
    "# print(y)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1989, 6)\n",
      "(1989,)\n",
      "(498, 6)\n",
      "(498,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Splitting the data into Training data & Testing data\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)      #Splitting the data into 80% for training & 20% for testing\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining the Linear Regression Model\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(X_train, y_train)      # Training the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Model score: 0.9630987574572458\n"
     ]
    }
   ],
   "source": [
    "# Score of the Linear Regression Model (Using the Test Data)\n",
    "\n",
    "linear_model_score = linear_model.score(X_test, y_test)\n",
    "print('Linear Model score:', linear_model_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Real & Prediction Values\n",
    "\n",
    "X_predict = np.array(df.drop(['Prediction'], axis=1))[-predict_days:]\n",
    "\n",
    "linear_model_predict_prediction = linear_model.predict(X_predict)\n",
    "linear_model_real_prediction = linear_model.predict(np.array(df.drop(['Prediction'], axis=1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining some Parameters\n",
    "\n",
    "predicted_dates = []\n",
    "recent_date = df.index.max()\n",
    "display_at = 1000\n",
    "alpha = 0.5\n",
    "\n",
    "for i in range(predict_days):\n",
    "    recent_date += str(timedelta(days=1))\n",
    "    predicted_dates.append(recent_date)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f53aa2ccd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Actual and Prediction Prices\n",
    "\n",
    "plt.figure(figsize=(15, 9))\n",
    "plt.plot(df.index[display_at:], linear_model_real_prediction[display_at:], label='Linear Prediction', color='blue', alpha=alpha)\n",
    "plt.plot(predicted_dates, linear_model_predict_prediction, label='Forecast', color='green', alpha=alpha)\n",
    "plt.plot(df.index[display_at:], df['Close'][display_at:], label='Actual', color='red')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AKASH\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.40599e-17): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining the Ridge Regression Model\n",
    "\n",
    "ridge_model = Ridge()\n",
    "ridge_model.fit(X_train, y_train)     # Training the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Model score: 0.9631029595686156\n"
     ]
    }
   ],
   "source": [
    "# Score of the Ridge Regression Model (Using the Test Data)\n",
    "\n",
    "ridge_model_score = ridge_model.score(X_test, y_test)\n",
    "print('Ridge Model score:', ridge_model_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define the Real & Prediction Values\n",
    "\n",
    "ridge_model_predict_prediction = ridge_model.predict(X_predict)\n",
    "ridge_model_real_prediction = ridge_model.predict(np.array(df.drop(['Prediction'], axis=1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f54016ccd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Actual and Prediction Prices\n",
    "\n",
    "plt.figure(figsize=(15, 9))\n",
    "plt.plot(df.index[display_at:], ridge_model_real_prediction[display_at:], label='Ridge Prediction', color='blue', alpha=alpha)\n",
    "plt.plot(predicted_dates, ridge_model_predict_prediction, label='Forecast', color='green', alpha=alpha)\n",
    "plt.plot(df.index[display_at:], df['Close'][display_at:], label='Actual', color='red')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AKASH\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+04, tolerance: 5.611e+02\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining the Lasso Regression Model\n",
    "\n",
    "lasso_model = Lasso()\n",
    "lasso_model.fit(X_train, y_train)     # Training the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Model score: 0.963218995993769\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Score of the Lasso Regression Model (Using the Test Data)\n",
    "\n",
    "lasso_model_score = lasso_model.score(X_test, y_test)\n",
    "print('Lasso Model score:', lasso_model_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Real & Prediction Values\n",
    "\n",
    "lasso_model_predict_prediction = lasso_model.predict(X_predict)\n",
    "lasso_model_real_prediction = lasso_model.predict(np.array(df.drop(['Prediction'],axis= 1)))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f542f2ccd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting the Actual and Prediction Prices\n",
    "\n",
    "plt.figure(figsize=(15, 9))\n",
    "plt.plot(df.index[display_at:], lasso_model_real_prediction[display_at:], label='Lasso Prediction', c='blue', alpha=alpha)\n",
    "plt.plot(predicted_dates, lasso_model_predict_prediction, label='Forecast', color='green', alpha=alpha)\n",
    "plt.plot(df.index[display_at:], df['Close'][display_at:], label='Actual', color='red')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Best Performer is Lasso Model with the score of 96.3218995993769%.\n"
     ]
    }
   ],
   "source": [
    "# Best Performance of the Regressor Models\n",
    "\n",
    "best_score = max(linear_model_score, ridge_model_score, lasso_model_score)\n",
    "index = np.argmax([linear_model_score, ridge_model_score, lasso_model_score])\n",
    "best_regressor = {0:'Linear Regression Model',\n",
    "                  1:'Ridge Model',\n",
    "                  2:'Lasso Model'}\n",
    "print(\"The Best Performer is {0} with the score of {1}%.\".format(best_regressor[index], best_score*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}